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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2501.12452 |
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| _version_ | 1866915114128506880 |
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| author | Bao, Ting Mao, Ning Duan, Wenhui Xu, Yong Del Maestro, Adrian Zhang, Yang |
| author_facet | Bao, Ting Mao, Ning Duan, Wenhui Xu, Yong Del Maestro, Adrian Zhang, Yang |
| contents | The integration of density functional theory (DFT) with machine learning enables efficient \textit{ab initio} electronic structure calculations for ultra-large systems. In this work, we develop a transfer learning framework tailored for long-wavelength moiré systems. To balance efficiency and accuracy, we adopt a two-step transfer learning strategy: (1) the model is pre-trained on a large dataset of computationally inexpensive non-twisted structures until convergence, and (2) the network is then fine-tuned using a small set of computationally expensive twisted structures. Applying this method to twisted MoTe$_2$, the neural network model generates the resulting Hamiltonian for a 1000-atom system in 200 seconds, achieving a mean absolute error below 0.1 meV. To demonstrate $O(N)$ scalability, we model nanoribbon systems with up to 0.25 million atoms ($\sim9$ million orbitals), accurately capturing edge states consistent with predicted Chern numbers. This approach addresses the challenges of accuracy, efficiency, and scalability, offering a viable alternative to conventional DFT and enabling the exploration of electronic topology in large scale moiré systems towards simulating realistic device architectures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_12452 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transfer learning electronic structure: millielectron volt accuracy for sub-million-atom moiré semiconductor Bao, Ting Mao, Ning Duan, Wenhui Xu, Yong Del Maestro, Adrian Zhang, Yang Materials Science Strongly Correlated Electrons The integration of density functional theory (DFT) with machine learning enables efficient \textit{ab initio} electronic structure calculations for ultra-large systems. In this work, we develop a transfer learning framework tailored for long-wavelength moiré systems. To balance efficiency and accuracy, we adopt a two-step transfer learning strategy: (1) the model is pre-trained on a large dataset of computationally inexpensive non-twisted structures until convergence, and (2) the network is then fine-tuned using a small set of computationally expensive twisted structures. Applying this method to twisted MoTe$_2$, the neural network model generates the resulting Hamiltonian for a 1000-atom system in 200 seconds, achieving a mean absolute error below 0.1 meV. To demonstrate $O(N)$ scalability, we model nanoribbon systems with up to 0.25 million atoms ($\sim9$ million orbitals), accurately capturing edge states consistent with predicted Chern numbers. This approach addresses the challenges of accuracy, efficiency, and scalability, offering a viable alternative to conventional DFT and enabling the exploration of electronic topology in large scale moiré systems towards simulating realistic device architectures. |
| title | Transfer learning electronic structure: millielectron volt accuracy for sub-million-atom moiré semiconductor |
| topic | Materials Science Strongly Correlated Electrons |
| url | https://arxiv.org/abs/2501.12452 |